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Updated: Apr 17, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

BicPAM: Pattern-based biclustering for biomedical data analysis.

Rui Henriques¹, Sara C Madeira¹

¹INESC-ID and Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.

Algorithms for Molecular Biology : AMB

|February 5, 2015

Summary

This summary is machine-generated.

BicPAM efficiently discovers diverse biclusters, including non-constant patterns, outperforming existing methods. This approach handles noise and missing data, offering novel biological insights from gene expression data.

Keywords:

Biclustering Biomedical data analysis Pattern mining

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Area of Science:

Bioinformatics
Computational Biology
Data Mining

Background:

Biclustering identifies groups of objects with coherent patterns across conditions, crucial for biomedical research.
Traditional methods often restrict bicluster types, limiting comprehensive analysis.
Pattern mining approaches discover flexible biclusters but are limited to constant patterns.

Purpose of the Study:

To introduce BicPAM, an efficient biclustering approach.
To enable exhaustive discovery of non-constant bicluster types (additive, multiplicative, symmetric).
To provide strategies for composing biclustering structures and handling data noise and missing values.

Main Methods:

BicPAM integrates state-of-the-art pattern-based biclustering principles.
It exhaustively mines non-constant bicluster types, including additive and multiplicative coherencies with symmetries.
The approach composes biclustering structures and manages noise and discretization.

Main Results:

BicPAM demonstrates superiority over existing methods.
It retrieves unique bicluster types and delivers exhaustive solutions.
The method successfully recovers planted biclusters in noisy datasets with missing values.

Conclusions:

BicPAM unifies pattern-based biclustering efforts.
It offers novel strategies for discovering biclusters with shifting, scaling, and symmetric assumptions.
The approach dynamically adapts to data with missing values and noise, yielding biologically relevant solutions.